This blog series Technology Highlight explores some of the ways that investments in innovation lead to better outcomes for infrastructure and public safety. When technology advances, it often becomes cheaper, more widely available and accessible, and helps facilitate faster, safer, and greener projects.
This series has highlighted many of the ways that innovation leads to greater access to technology. Narrowing into damage prevention, we’ve explored the way technology at One-Call centers, through locator equipment, and for onsite communication improves safety outcomes. The focus on damage prevention is critical, because it is the foundation of all infrastructure work. In order to build, we must break ground. And when we do, we risk exposing and damaging the subsurface infrastructure already in place! As we zoom further out in this technology highlight series, we see ways that innovative solutions around the broader infrastructure and industrial sphere.
In this Technology Highlight, we will focus on Artificial Intelligence. When most people think of artificial intelligence (AI), many may think of the Terminator movies or the robots from Star Wars. While robots that fully resemble humans are not a reality yet, AI is changing the way that industries produce goods, avert deficiencies, and identify challenges in their fields.
Innovations do come with their own set of complexities and terms like machine learning (ML) and AI are often used interchangeably. The reality is that ML refers to a type of AI that is developed for a narrower set of tasks.
Artificial intelligence can be thought of as “a machine that seems almost human-like and can imitate human behavior… includ[ing] problem-solving, learning, and planning.” AI analyzes information and identifies patterns in a dataset to replicate the pattern identification process. It is essentially smart software, operating in the digital space, and performing tasks.
The term artificial intelligence is an umbrella term for any type of technology that is capable of these skills. Any system that makes decisions that normally require a human level of expertise and coordination fall into the category of AI. These systems could range from an autonomous mechanism on a product assembly line to the system in your car that detects other vehicles and objects.
One version of AI that people interact with daily is during the writing, sending, and receiving of an email. When you type an email, AI like spellcheck help by underlining misspelled words and awkward grammar. When an email is received, AI within your email account identifies whether it comes from a frequent, new, or suspicious source and sorts it accordingly. Finally, antivirus software within an email prevents certain accounts from delivering an email. Messages with similar patterns to common malware or phishing will be flagged with a warning.
Machine learning (ML) is a branch of AI that focuses on utilizing data and algorithms to copy the way humans learn over time, thereby improving the machine’s decision-making accuracy.
How ML works can be broken into three steps:
- The ML algorithm uses data to form an estimate or prediction about a pattern.
- An error function process determines if the prediction is accurate based on previous predictions.
- The estimated model and known input are adjusted until they are as close as possible to one another.
This process is repeated until the ML system is as accurate as possible. The most common type of ML that most people are familiar with is the Netflix recommended search engine.
The Netflix ML algorithm goes over all the movies that you’ve watched and displays other similar movies. For example, if a Netflix user watches movies about animals, the Netflix recommended queue might display nature documentaries. The system can also detect other commonalities like genres or actors or actresses.
The nuances between AI and ML are crucial to understanding their real-world applications. AI can range from simple email spell-checks to autonomous Roomba vacuum cleaners. ML algorithms provide in-depth potential to solve problems by analyzing datasets in seconds to inform a decision. The different types of AI make identifying products that could revolutionize different economic sectors difficult. However, by distinguishing between AI and a subset like ML, business and government leaders can better identify the systems that could most benefit their industries.
The applications of AI extent far beyond email and Netflix – they help build, maintain, and protect critical infrastructure every day. Even in the damage prevention space, where excavators and locators collaborate to avoid damage to physical infrastructure under the dirt, AI has enormous implications.
In the next blog, we will explore how AI integration into technology across the damage prevention process, helps alert operators to risk, damage, and more.
Written by Roy Mathews, Public Policy Associate
Interested in other technology highlights? Stay tuned for more ways technology is making damage prevention safer.
The Alliance for Innovation and Infrastructure (Aii) is an independent, national research and educational organization. An innovative think tank, Aii explores the intersection of economics, law, and public policy in the areas of climate, damage prevention, energy, infrastructure, innovation, technology, and transportation.